{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Simulation Results for varying number of maximum iterations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook shows BER and Sum Capacity results for different IA\n",
      "algorithms when the maximum number of allowed iterations is limited.  Note\n",
      "that the algorithm might run less iterations than the allowed maximum if\n",
      "the precoders do not change significantly from one iteration to the next\n",
      "one.  The maximum number of allowed iterations vary from 5 to 60, except\n",
      "for the closed form algorithm, which is not iterative. The solid lines\n",
      "indicate the BER or Sum Capacity in the left axis, while the dashed lines\n",
      "indicate the mean number of iterations that algorithm used.\n",
      "\n",
      "Let's perform some initializations.\n",
      "\n",
      "First we enable the \"inline\" mode for plots."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we import some modules we use and add the PyPhysim to the python path."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "sys.path.append(\"/home/darlan/cvs_files/pyphysim\")\n",
      "# xxxxxxxxxx Import Statements xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "from util.simulations import SimulationRunner, SimulationParameters, SimulationResults, Result\n",
      "from comm import modulators, channels\n",
      "from util.conversion import dB2Linear\n",
      "from util import misc\n",
      "from ia import ia\n",
      "import numpy as np\n",
      "from pprint import pprint\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we set the transmit parameters and load the simulation results from the file corresponding to those transmit parameters."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# xxxxx Parameters xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "#params = SimulationParameters.load_from_config_file('ia_config_file.txt')\n",
      "K = 3\n",
      "Nr = 6\n",
      "Nt = 2\n",
      "Ns = 2\n",
      "M = 4\n",
      "modulator = \"PSK\"\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# xxxxx Results base name xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "base_name = 'results_{M}-{modulator}_{Nr}x{Nt}_({Ns})_MaxIter_[120]'.format(M=M, modulator=modulator, Nr=Nr, Nt=Nt, Ns=Ns)\n",
      "base_name_no_iter = 'results_{M}-{modulator}_{Nr}x{Nt}_({Ns})_MaxIter_[120]'.format(M=M, modulator=modulator, Nr=Nr, Nt=Nt, Ns=Ns)  # Used only for the closed form algorithm, which is not iterative\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "alt_min_results = SimulationResults.load_from_file(\n",
      "    'ia_alt_min_{0}.pickle'.format(base_name))\n",
      "closed_form_results = SimulationResults.load_from_file(\n",
      "    'ia_closed_form_{0}.pickle'.format(base_name_no_iter))\n",
      "# closed_form_first_results = SimulationResults.load_from_file(\n",
      "#     'ia_closed_form_first_init_{0}.pickle'.format(base_name))\n",
      "# max_sinrn_results = SimulationResults.load_from_file(\n",
      "#     'ia_max_sinr_{0}.pickle'.format(base_name))\n",
      "# min_leakage_results = SimulationResults.load_from_file(\n",
      "#     'ia_min_leakage_{0}.pickle'.format(base_name))\n",
      "mmse_results = SimulationResults.load_from_file(\n",
      "    'ia_mmse_{0}.pickle'.format(base_name))\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's define helper methods to get mean number of IA iterations from a simulation results object."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Helper function to get the number of repetitions for a given set of transmit parameters\n",
      "def get_num_runned_reps(sim_results_object, fixed_params=dict()):\n",
      "    all_runned_reps = np.array(sim_results_object.runned_reps)\n",
      "    indexes = sim_results_object.params.get_pack_indexes(fixed_params)\n",
      "    return all_runned_reps[indexes]\n",
      "\n",
      "# Helper function to get the number of IA runned iterations for a given set of transmit parameters\n",
      "def get_num_mean_ia_iterations(sim_results_object, fixed_params=dict()):\n",
      "    return sim_results_object.get_result_values_list('ia_runned_iterations', fixed_params)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get the SNR values from the simulation parameters object."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SNR_alt_min = np.array(alt_min_results.params['SNR'])\n",
      "SNR_closed_form = np.array(closed_form_results.params['SNR'])\n",
      "# SNR_max_SINR = np.array(max_sinrn_results.params['SNR'])\n",
      "# SNR_min_leakage = np.array(min_leakage_results.params['SNR'])\n",
      "SNR_mmse = np.array(mmse_results.params['SNR'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Define a function that we can call to plot the BER.\n",
      "This function will plot the BER for all SNR values for the four IA algorithms, given the desired \"max_iterations\" parameter value."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_ber(max_iterations, ax=None):\n",
      "    ber_alt_min = alt_min_results.get_result_values_list(\n",
      "        'ber',\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    ber_CF_alt_min = alt_min_results.get_result_values_confidence_intervals(\n",
      "        'ber',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    ber_errors_alt_min = np.abs([i[1] - i[0] for i in ber_CF_alt_min])\n",
      "\n",
      "    ber_closed_form = closed_form_results.get_result_values_list(\n",
      "        'ber',\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    ber_CF_closed_form = closed_form_results.get_result_values_confidence_intervals(\n",
      "        'ber',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    ber_errors_closed_form = np.abs([i[1] - i[0] for i in ber_CF_closed_form])\n",
      "\n",
      "    # ber_closed_form_first = closed_form_first_results.get_result_values_list('ber')\n",
      "    # ber_CF_closed_form_first = closed_form_first_results.get_result_values_confidence_intervals('ber', P=95)\n",
      "    # ber_errors_closed_form_first = np.abs([i[1] - i[0] for i in ber_CF_closed_form_first])\n",
      "\n",
      "    # ber_max_sinr = max_sinrn_results.get_result_values_list(\n",
      "    #     'ber',\n",
      "    #     fixed_params={'max_iterations': max_iterations})\n",
      "    # ber_CF_max_sinr = max_sinrn_results.get_result_values_confidence_intervals(\n",
      "    #     'ber',\n",
      "    #     P=95,\n",
      "    #     fixed_params={'max_iterations': max_iterations})\n",
      "    # ber_errors_max_sinr = np.abs([i[1] - i[0] for i in ber_CF_max_sinr])\n",
      "\n",
      "    # ber_min_leakage = min_leakage_results.get_result_values_list('ber')\n",
      "    # ber_CF_min_leakage = min_leakage_results.get_result_values_confidence_intervals('ber', P=95)\n",
      "    # ber_errors_min_leakage = np.abs([i[1] - i[0] for i in ber_CF_min_leakage])\n",
      "\n",
      "    ber_mmse = mmse_results.get_result_values_list(\n",
      "        'ber',\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    ber_CF_mmse = mmse_results.get_result_values_confidence_intervals(\n",
      "        'ber',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    ber_errors_mmse = np.abs([i[1] - i[0] for i in ber_CF_mmse])\n",
      "\n",
      "    if ax is None:\n",
      "        fig, ax = plt.subplots(nrows=1, ncols=1)\n",
      "    ax.errorbar(SNR_alt_min, ber_alt_min, ber_errors_alt_min, fmt='-r*', elinewidth=2.0, label='Alt. Min.')\n",
      "    ax.errorbar(SNR_closed_form, ber_closed_form, ber_errors_closed_form, fmt='-b*', elinewidth=2.0, label='Closed Form')\n",
      "    # ax.errorbar(SNR_max_SINR, ber_max_sinr, ber_errors_max_sinr, fmt='-g*', elinewidth=2.0, label='Max SINR')\n",
      "    # ax.errorbar(SNR, ber_min_leakage, ber_errors_min_leakage, fmt='-k*', elinewidth=2.0, label='Min Leakage.')\n",
      "    ax.errorbar(SNR_mmse, ber_mmse, ber_errors_mmse, fmt='-m*', elinewidth=2.0, label='MMSE.')\n",
      "\n",
      "    ax.set_xlabel('SNR')\n",
      "    ax.set_ylabel('BER')\n",
      "    title = 'BER for Different Algorithms ({max_iterations} Max Iterations)\\nK={K}, Nr={Nr}, Nt={Nt}, Ns={Ns}, {M}-{modulator}'.replace(\"{max_iterations}\", str(max_iterations))\n",
      "    ax.set_title(title.format(**alt_min_results.params.parameters))\n",
      "\n",
      "    ax.set_yscale('log')\n",
      "    leg = ax.legend(fancybox=True, shadow=True, loc='lower left', bbox_to_anchor=(0.01, 0.01), ncol=4)\n",
      "    ax.grid(True, which='both', axis='both')\n",
      "    \n",
      "    # Lets plot the mean number of ia iterations\n",
      "    ax2 = ax.twinx()\n",
      "    mean_alt_min_ia_terations = get_num_mean_ia_iterations(alt_min_results, {'max_iterations': max_iterations})\n",
      "    # mean_max_sinrn_ia_terations = get_num_mean_ia_iterations(max_sinrn_results, {'max_iterations': max_iterations})\n",
      "    mean_mmse_ia_terations = get_num_mean_ia_iterations(mmse_results, {'max_iterations': max_iterations})\n",
      "    ax2.plot(SNR_alt_min, mean_alt_min_ia_terations, '--r*')\n",
      "    # ax2.plot(SNR_max_SINR, mean_max_sinrn_ia_terations, '--g*')\n",
      "    ax2.plot(SNR_mmse, mean_mmse_ia_terations, '--m*')\n",
      "    \n",
      "    # Horizontal line with the max alowed ia iterations\n",
      "    ax2.hlines(max_iterations, SNR_alt_min[0], SNR_alt_min[-1], linestyles='dashed')\n",
      "    ax2.set_ylim(0, max_iterations*1.1)\n",
      "    ax2.set_ylabel('IA Mean Iterations')\n",
      "\n",
      "    # Set the X axis limits\n",
      "    ax.set_xlim(SNR_alt_min[0], SNR_alt_min[-1])\n",
      "    # Set the Y axis limits\n",
      "    ax.set_ylim(1e-6, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Plot the BER"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can create a 4x4 grids if plots and call the plot_ber function to plot in each subplot."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = subplots(1,1,figsize=(10,7.5))\n",
      "plot_ber(120, ax)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAoIAAAHkCAYAAACqtU7PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5116290>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Plot the Capacity"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_capacity(max_iterations, ax=None):\n",
      "    # xxxxx Plot Sum Capacity (all) xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "    sum_capacity_alt_min = alt_min_results.get_result_values_list(\n",
      "        'sum_capacity',\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    sum_capacity_CF_alt_min = alt_min_results.get_result_values_confidence_intervals(\n",
      "        'sum_capacity',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    sum_capacity_errors_alt_min = np.abs([i[1] - i[0] for i in sum_capacity_CF_alt_min])\n",
      "\n",
      "    sum_capacity_closed_form = closed_form_results.get_result_values_list(\n",
      "        'sum_capacity',\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    sum_capacity_CF_closed_form = closed_form_results.get_result_values_confidence_intervals(\n",
      "        'sum_capacity',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    sum_capacity_errors_closed_form = np.abs([i[1] - i[0] for i in sum_capacity_CF_closed_form])\n",
      "\n",
      "    # sum_capacity_max_sinr = max_sinrn_results.get_result_values_list(\n",
      "    #     'sum_capacity',\n",
      "    #     fixed_params={'max_iterations': max_iterations})\n",
      "    # sum_capacity_CF_max_sinr = max_sinrn_results.get_result_values_confidence_intervals(\n",
      "    #     'sum_capacity',\n",
      "    #     P=95,\n",
      "    #     fixed_params={'max_iterations': max_iterations})\n",
      "    # sum_capacity_errors_max_sinr = np.abs([i[1] - i[0] for i in sum_capacity_CF_max_sinr])\n",
      "\n",
      "    # sum_capacity_min_leakage = min_leakage_results.get_result_values_list('sum_capacity')\n",
      "    # sum_capacity_CF_min_leakage = min_leakage_results.get_result_values_confidence_intervals('sum_capacity', P=95)\n",
      "    # sum_capacity_errors_min_leakage = np.abs([i[1] - i[0] for i in sum_capacity_CF_min_leakage])\n",
      "\n",
      "    sum_capacity_mmse = mmse_results.get_result_values_list(\n",
      "        'sum_capacity',\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    sum_capacity_CF_mmse = mmse_results.get_result_values_confidence_intervals(\n",
      "        'sum_capacity',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    sum_capacity_errors_mmse = np.abs([i[1] - i[0] for i in sum_capacity_CF_mmse])\n",
      "\n",
      "    if ax is None:\n",
      "        fig, ax = plt.subplots(nrows=1, ncols=1)\n",
      "    # xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "    ax.errorbar(SNR_alt_min, sum_capacity_alt_min, sum_capacity_errors_alt_min, fmt='-r*', elinewidth=2.0, label='Alt. Min.')\n",
      "    ax.errorbar(SNR_closed_form, sum_capacity_closed_form, sum_capacity_errors_closed_form, fmt='-b*', elinewidth=2.0, label='Closed Form')\n",
      "    # ax.errorbar(SNR_max_SINR, sum_capacity_max_sinr, sum_capacity_errors_max_sinr, fmt='-g*', elinewidth=2.0, label='Max SINR')\n",
      "    # ax.errorbar(SNR, sum_capacity_min_leakage, sum_capacity_errors_min_leakage, fmt='-k*', elinewidth=2.0, label='Min Leakage.')\n",
      "    ax.errorbar(SNR_mmse, sum_capacity_mmse, sum_capacity_errors_mmse, fmt='-m*', elinewidth=2.0, label='MMSE.')\n",
      "    # xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "    ax.set_xlabel('SNR')\n",
      "    ax.set_ylabel('Sum Capacity')\n",
      "    title = 'Sum Capacity for Different Algorithms ({max_iterations} Max Iterations)\\nK={K}, Nr={Nr}, Nt={Nt}, Ns={Ns}, {M}-{modulator}'.replace(\"{max_iterations}\", str(max_iterations))\n",
      "    ax.set_title(title.format(**alt_min_results.params.parameters))\n",
      "\n",
      "    #leg = ax.legend(fancybox=True, shadow=True, loc=2)\n",
      "    leg = ax.legend(fancybox=True, shadow=True, loc='lower right', bbox_to_anchor=(0.99, 0.01), ncol=4)\n",
      "    \n",
      "    ax.grid(True, which='both', axis='both')\n",
      "    \n",
      "    # Lets plot the mean number of ia iterations\n",
      "    ax2 = ax.twinx()\n",
      "    mean_alt_min_ia_terations = get_num_mean_ia_iterations(alt_min_results, {'max_iterations': max_iterations})\n",
      "    # mean_max_sinrn_ia_terations = get_num_mean_ia_iterations(max_sinrn_results, {'max_iterations': max_iterations})\n",
      "    mean_mmse_ia_terations = get_num_mean_ia_iterations(mmse_results, {'max_iterations': max_iterations})\n",
      "    ax2.plot(SNR_alt_min, mean_alt_min_ia_terations, '--r*')\n",
      "    # ax2.plot(SNR_max_SINR, mean_max_sinrn_ia_terations, '--g*')\n",
      "    ax2.plot(SNR_mmse, mean_mmse_ia_terations, '--m*')\n",
      "    \n",
      "    # Horizontal line with the max alowed ia iterations\n",
      "    ax2.hlines(max_iterations, SNR_alt_min[0], SNR_alt_min[-1], linestyles='dashed')\n",
      "    ax2.set_ylim(0, max_iterations*1.1)\n",
      "    ax2.set_ylabel('IA Mean Iterations')\n",
      "\n",
      "    # Set the X axis limits\n",
      "    ax.set_xlim(SNR_alt_min[0], SNR_alt_min[-1])\n",
      "    # Set the Y axis limits\n",
      "    #ax.set_ylim(1e-6, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = subplots(1,1,figsize=(10,7.5))\n",
      "plot_capacity(120, ax)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAnoAAAHkCAYAAACpLoKiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7d3e810>"
       ]
      }
     ],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}